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Summarizing and Comparing Story Plans

Authors Adam Amos-Binks, David L. Roberts, R. Michael Young

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Adam Amos-Binks
David L. Roberts
R. Michael Young

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Adam Amos-Binks, David L. Roberts, and R. Michael Young. Summarizing and Comparing Story Plans. In 7th Workshop on Computational Models of Narrative (CMN 2016). Open Access Series in Informatics (OASIcs), Volume 53, pp. 9:1-9:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)
https://doi.org/10.4230/OASIcs.CMN.2016.9

Abstract

Branching story games have gained popularity for creating unique playing experiences by adapting story content in response to user actions. Research in interactive narrative (IN) uses automated planning to generate story plans for a given story problem. However, a story planner can generate multiple story plan solutions, all of which equally-satisfy the story problem definition but contain different story content. These differences in story content are key to understanding the story branches in a story problem's solution space, however we lack narrative-theoretic metrics to compare story plans. We address this gap by first defining a story plan summarization model to capture the important story semantics from a story plan. Secondly, we define a story plan comparison metric that compares story plans based on the summarization model. Using the Glaive narrative planner and a simple story problem, we demonstrate the usefulness of using the summarization model and distance metric to characterize the different story branches in a story problem's solution space.
Keywords
  • artifical intelligence
  • planning
  • narrative
  • comparison
  • story

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